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Developing an Image Morphing Approach for Visualization of Digital Twin Liver Fat ReductionGustafsson, Peter January 2022 (has links)
Nonalcoholic liver steatosis (NALS) is a condition where fat infiltrates the tissue of the liver and accumulates in droplets. While not a dangerous condition on its own, if left for long enough it can develop into conditions which could cause serious and potentially permanent damage to the liver. One of the primary approaches for preventing NALS from progressing is through changes in diet and lifestyle. However, explaining to a patient the impact of such a change can be difficult, which hampers motivation in many instances. Digital twin technology can provide simulations of what will happen to the body after a lifestyle change, but the output data is very abstract and can thus be a challenge to convey properly to a patient. In this project I investigate a digital data visualization approach where a photo of a liver sample is morphed to showcase liver fat droplets shrinking as a result of a changed lifestyle, as simulated by the digital twin. The approach uses a simple image morphing algorithm that pulls pixel intensity values from regions designated by a morph field and composites a newimage from the updated values. By selectively choosing regions of interest to pull pixels towards or away from, with a ramping cutoff in morph field strength, it is possible to designate certain regions in the image to be morphed. The program is capable of generating time series of increasingly morphed images in both greyscale and truecolour, and it can save the time series as an animated .GIF file, with linear interpolation between the morphed images in the time series.
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Deep learning on large neuroimaging datasetsJönemo, Johan January 2024 (has links)
Magnetic resonance imaging (MRI) is a medical imaging method that has become increasingly more important during the last 4 decades. This is partly because it allows us to acquire a 3D-representation of a part of the body without exposing patients to ionizing radiation. Furthermore, it also typically gives better contrast between soft tissues than x-ray based techniques such as CT. The image acquisition procedure of MRI is also much more flexible. One can vary the signal sequence, not only to change how different types of tissue map to different intensities, but also to measure flow, diffusion or even brain activity over time. Machine learning has gained great impetus the last decade and a half. This is probably partly because of the work done on the mathematical foundations of machine learning done at the end of last century in conjunction with the availability of specialized massively parallel processors, originally developed as graphical processing units (GPUs), which are ideal for training or running machine learning models. The work presented in this thesis combines MRI and machine learning in order to leverage the large amounts of MRI-data available in open data sets, to address questions of clinical relevance about the brain. The thesis comprises three studies. In the first one the subproblem which augmentation methods are useful in the larger context of classifying autism, was investigated. The second study is about predicting brain age. In particular it aims to construct light-weight models using the MRI volumes in a condensed form, so that the model can be trained in a short time and still reach good accuracy. The third study is a development of the previous that investigates other ways of condensing the brain volumes. / Magnetresonansavbildningar, ofta kallat MR eller MRI, är en bilddiagnostik-metod som har blivit allt viktigare under de senaste 40 åren. Detta på grund av att man kan erhålla 3D-bilder av kroppsdelar utan att utsätta patienter för joniserande strålning. Dessutom får man typiskt bättre kontraster mellan mjukdelar än man får med motsvarande genomlysningsmetod (CT, eller 3D röntgen). Själva bildinsamlingsförfarandet är också mera flexibelt med MR. Man kan genom att ändra program för utsända och registrerade signa-ler, inte bara ändra vad som framförallt framträder på bilden (t.ex. vatten, fett, H-densitet, o.s.v.) utan även mäta flöde och diffusion eller till och med hjärnaktivitet över tid. Maskininlärning har fått ett stort uppsving under 2010-talet, dels på grund av utveckling av teknologin för att träna och konstruera maskininlärningsmodeller dels på grund av tillgängligheten av massivt parallella specialprocessorer – initialt utvecklade för att generera datorgrafik. Detta arbete kombinerar MR med maskininlärning, för att dra nytta av de stora mängder MR data som finns samlad i öppna databaser, för att adressera frågor av kliniskt intresse angående hjärnan. Avhandlingen innehåller tre studier. I den första av dessa undersöks del-problemet vilken eller vilka metoder för att artificiellt utöka träningsdata som är bra vid klassificering om en person har autism. Det andra arbetet adresserar bedömning av så kallad "hjärn-ålder". Framför allt strävar arbetet efter att hitta lättviktsmodeller som använder en komprimerad form av varje hjärnvolym, och därmed snabbt kan tränas till att bedöma en persons ålder från en MR-volym av hjärnan. Det tredje arbetet utvecklar modellen från det föregående genom att undersöka andra typer av komprimering. / <p><strong>Funding:</strong> This research was supported by the Swedish research council (2017-04889), the ITEA/VINNOVA project ASSIST (Automation, Surgery Support and Intuitive 3D visualization to optimize workflow in IGT SysTems, 2021-01954), and the Åke Wiberg foundation (M20-0031, M21-0119, M22-0088).</p>
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The accuracy, reliability, and practicality of Convolutional Neural Networks in classifying ultrasound images for improved breast cancer diagnosisQvarnlöf, Moa January 2024 (has links)
Traditionally, analysing images for patient diagnosis and personalised treatment planning hasbeen reliant only on human expertise. Now, with growing data volumes and the need for fasterprocessing, artificial intelligence (AI) has increased in importance, revolutionising medical imageanalysis.In this project, a computer-aided diagnosis (CAD) system using deep learning models (DLMs)was developed for ultrasound (US) breast cancer images. Two datasets were combined andused for model training and evaluation. The first dataset was older, larger, and moreestablished, while the second was smaller and recently published at the project start. Bothdatasets contained benign, malignant and normal cases, with an US image and a mask file foreach case. The mask file contained the segmented lesion in the US image. Two differentapproaches were used in this project. The first approach used only US images to train andevaluate models. The second approach created an overlaid image from the US image andmask file for each case, and inputted the overlay image to the model.Different techniques, including class weighting, data augmentation, and pre-processing, wereexplored to address class imbalance and enhance model performance. Class weighting anddata augmentation were both shown to even out class performance. Results indicate thataccuracy and minority class recall can be improved with pre-processing and data augmentation.Hyperparameter tuning optimised the model performance further. Approach 1 achieved anaccuracy of 83%, AUC of 87%, benign and normal recalls of 77%, and malignant recall of 95%.Approach 2 achieved an accuracy of 94%, AUC of 96%, benign recall of 97%, malignant recallof 87% and normal recall of 100%.For CAD systems to reach their full potential, they must be reliable and easy to use andinterpret for medical professionals. The field of CAD systems for US breast cancer imagesremains challenged by the lack of public comprehensive datasets. Models must be able togeneralise to diverse patient populations, and future work should focus on larger and morediverse datasets.
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Deep Learning to Enhance Fluorescent Signals in Live Cell ImagingForsgren, Edvin January 2020 (has links)
No description available.
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Konventionell röntgen versus datortomografi vid pelvimetri : -En systematisk litteraturstudie / Conventional x-ray versus computer tomography on pelvimetryBorg, Anton, Padjen, Haris January 2017 (has links)
No description available.
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Hälsoeffekter hos MR-personal vid exponering av magnetfält : En litteraturstudie / Health effects to MRI-personnel when exposed to magnetic fields : A literature studyAndersson, Amanda, Wallenborg, Jenny January 2019 (has links)
No description available.
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Image Analysis and Visualization of the Human Mastoid Air Cell SystemCros, Olivier January 2015 (has links)
From an engineering background, it is often believed that the human anatomy has already been fully described. Radiology has greatly contributed to understand the inside of the human body without surgical intervention. Despite great advances in clinical CT scanning, image quality is still related to a limited amount X-ray exposure for the patient safety. This limitation prevents fine anatomical structures to be visible and, more importantly, to be detected. Where such modality is of great advantage for screening patients, extracting parameters like surface area and volume implies the bone structure to be large enough in relation to the scan resolution. The mastoid, located in the temporal bone, houses an air cell system whose cells have a variation in size that can go far below current conventional clinical CT scanner resolution. Therefore, the mastoid air cell system is only partially represented on a CT scan. Any statistical analysis will be biased towards air cells of smaller size. To allow a complete representation of the mastoid air cell system, a micro-CT scanner is more adequate. Micro-CT scanning uses approximately the same amount of X-rays but for a much longer exposure time compared to what is normally allowed for patients. Human temporal bone specimens are therefore necessary when using such scanning method. Where the conventional clinical CT scanner lacks level of minutes details, micro-CT scanning provides an overwhelming amount of fine details. Prior to any image analysis of medical data, visualization of the data is often needed to learn how to extract the structures of interest for further processing. Visualization of micro-CT scans is of no exception. Due to the high resolution nature of the data, visualization of such data not only requires modern and powerful computers, but also necessitates a tremendous amount of time to adjust the hiding of irrelevant structures, to find the correct orientation, while emphasising the structure of interest. Once the quality of the data has been assessed, and a strategy for the image processing has been decided, the image processing can start, to in turn extract metrics such as the surface area or volume and draw statistics from it. The temporal bone being one of the most complex in the human body, visualization of micro-CT scanning of this bone awakens the curiosity of the experimenter, especially with the correct visualization settings. This thesis first presents a statistical analysis determining the surface area to volume ratio of the mastoid air cell system of human temporal bone, from micro-CT scanning using methods previously applied for conventional clinical CT scannings. The study compared current resul s with previous studies, with successive downsampling the data down to a resolution found in conventional clinical CT scanning. The results from the statistical analysis showed that all the small mastoid air cells, that cannot be detected in conventional clinical CT scans, do heavily contribute to the estimation of the surface area, and in consequence to the estimation of the surface area to volume ratio by a factor of about 2.6. Such a result further strengthens the idea of the mastoid to play an active role in pressure regulation and gas exchange. Discovery of micro-channels through specific use of a non-traditional transfer function was then reported, where a qualitative and a quantitative preanalysis was performed are described. To gain more knowledge about these micro-channels, a local structure tensor analysis was applied where structures are described in terms of planar, tubular, or isotropic structures. The results from this structural tensor analysis, also reported in this thesis, suggest these micro-channels to potentially be part of a more complex framework, which hypothetically would provide a separate blood supply for the mucosa lining the mastoid air cell system.
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Vision guided robot : a solution för parts handling - SAAB AutmobileRamos, Wilbert Rivera January 2007 (has links)
No description available.
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HDR och Tone mapping i automatiserade tullsystem / HDR and Tone mapping in automated toll systemsLarsson, Kristian, Larsson, Michael January 2013 (has links)
This report is about how HDR (HighDynamicRange) can be created and used in combination with Tone mapping. This work has been carried out together with Kapsch TrafficCom AB in Jönköping. The objective of this project is to: Evaluate and investigate the effects given to pictures by HDR and tone mapping. Evaluate if the technology may lead to improvements in Kapsch’s systems. To construct a program which is able to handle some form of tone mapping or HDR-algorithm. These questions will be answered in this report: What kind of effects has HDR and tone mapping-algorithms on pictures? Can the HDR-technology give better data in Kapsch’s systems? The research method used in this report is called action research. This means the authors has investigated the technology by reading different documentations and by testing different algorithms to see what kind of result they give. The report describes some of the tests made to see if the technology is appropriate in Kapsch’s system.There is two smaller reports made by the authors which documenting some of the work.The first report describes the work with different settings for a camera to create pictures with HDR-quality. The second report describes the differences between tone mapping-algorithms and different file format. Both reports are included as appendices to this report.In the program created by the authors some larger library’s with standard functions for opening of JPEG-pictures was used. The chosen library’s was MFC and GDI+. The program is developed for a windows environment and handles functions like sharpening with unsharp mask, colour space conversion and tone mapping.
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Vision guided robot : a solution för parts handling - SAAB AutmobileRamos, Wilbert Rivera January 2007 (has links)
No description available.
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